Table of contents
- pATLAS API
Plasmid Atlas is a web-base tool that empowers
researchers to easily and rapidly access
information related with plasmids present in
NCBI's refseq database.
In pATLAS each node (or circle) represents
a plasmid and each link between two plasmids means that those two plasmids
share around 90% average nucleotide identity.
With this tool we have two main goals:
- Increase the accessibility of plasmid relevant metadata to users as well as facilitate the access to that metadata.
- Improve the ease of interpreting results from High Throughput Sequencing (HTS) for plasmid detection.
Tiago F Jesus, Bruno Ribeiro-Gonçalves, Diogo N Silva, Valeria Bortolaia, Mário Ramirez, João A Carriço; Plasmid ATLAS: plasmid visual analytics and identification in high-throughput sequencing data, Nucleic Acids Research, gky1073, https://doi.org/10.1093/nar/gky1073
If are interested in learning how to use pATLAS, please refer to gitbook documentation.
Postgresql (>= 10.0) - This script uses Postgres database to store the database: releases page
Python 3 and the respective pip.
To install all other dependencies just run: pip install -r requirements.txt
MASHix.py is the main script to generate the database. This script generates a matrix of pairwise comparisons between sequences in input fasta(s) file(s). Note that it reads multifastas, i.e., each header in fasta is a reference sequence.
'-i','--input_references' - 'Provide the input fasta files to parse. This will inputs will be joined in a master fasta.' '-o','--output' - 'Provide an output tag' '-t', '--threads' - 'Provide the number of threads to be used' '-db', '--database_name' - 'This argument must be provided as the last argument. It states the database name that must be used.'
MASH related options:
'-k','--kmers' - 'Provide the number of k-mers to be provided to mash sketch. Default: 21' '-p','--pvalue' - 'Provide the p-value to consider a distance significant. Default: 0.05' '-md','--mashdist' - 'Provide the maximum mash distance to be parsed to the matrix. Default:0.1'
'-no_rm', '--no-remove' - 'Specify if you do not want to remove the output concatenated fasta.' '-hist', '--histograms' - 'Checks the distribution of distances values ploting histograms.' '-non', '--nodes_ncbi' - 'specify the path to the file containing nodes.dmp from NCBI' '-nan', '--names_ncbi' - 'specify the path to the file containing names.dmp from NCBI' '--search-sequences-to-remove' - 'this option allows to only run the part of the script that is required to generate the filtered fasta. Allowing for instance to debug sequences that shoudn't be removed using 'cds' and 'origin' keywords'.
I don't like database name! How do I change it?
db_manager/config_default.py and edit the following line:
SQLALCHEMY_DATABASE_URI = 'postgresql:///<custom_database_name>'
I don't like table name inside database! How do I change it?
Go to db_manager/db_app/models.py and edit the following line:
__tablename__ = "<custom_table_name>"
Database migration from one server to another
pg_dump <db_name> > <file_name.sql>
psql -U <user_name> -d <db_name> -f <file_name.sql>
This script inherits a class from ODiogoSilva/Templates and uses it to parse abricate outputs and dumps abricate outputs to a psql database, depending on the input type provided.
"-i", "--input_file" - "Provide the abricate file to parse to db. It can accept more than one file in the case of resistances." "-db_psql", "--database_name" - "his argument must be provided as the last argument. It states the database name that must be used." "-db", "--db" - "Provide the db to output in psql models." "-id", "--identity" - "minimum identity to be reported to db" "-cov", "--coverage" - "minimum coverage do be reported to db" "-csv", "--csv" - "Provide card csv file to get correspondence between DNA accessions and ARO accessions. Usually named aro_index.csv. By default this file is already available in patlas repo with a specific path: 'db_manager/db_app/static/csv/aro_index.csv'"
This script is located in
utils folder and can be used to generate a
JSON file with the corresponding taxonomic tree. It fetches for a given
species, the genera, family and order to which it belongs.
Note: for plasmids I have to make some filtering in the resulting taxids
and list of species that other users may want to skip
-i INPUT_LIST, --input_list INPUT_LIST provide a file with a listof species. Each speciesshould be in each line. -non NODES_FILE, --nodes_ncbi NODES_FILE specify the path to the file containing nodes.dmp from NCBI -nan NAMES_FILE, --names_ncbi NAMES_FILE specify the path to the file containing names.dmp from NCBI -w, --weirdos This option allows the userto add a checks for weirdentries. This is mainly usedto parse the plasmids refseq, so if you do not want this to be used, use this option.
List of entries that will be filtered from
From taxonomy levels:
From species in fasta headers:
It also attempts to fix some bugs in species naming like the following:
- "B bronchiseptica"
- "S pyogenes"
Note: Yes people like to give interesting names to bacteria...
Schematics of the pATLAS database creation
Workflow for database creation
- Download plasmid sequences available in NCBI refseq
- Extract fasta from tar.gz
- Download and extract NCBI taxonomy, which will be fed to pATLAS.
- Clone this repository:
git clone https://github.com/tiagofilipe12/pATLAS
Install its dependencies
Configure the database:
createdb <database_name> pATLAS/patlas/db_manager/db_create.py <database_name>
- run MASHix.py - the output will include a filtered
fasta file (
- run ABRicate, with CARD, ResFinder, PlasmidFinder, VFDB databases.
# e.g. abricate --db card <master_fasta*.fas> > abr_card.tsv abricate --db resfinder <master_fasta*.fas> > abr_resfinder.tsv abricate --db vfdb <master_fasta*.fas> > abr_vfdb.tsv abricate --db plasmidfinder <master_fasta*.fas> > abr_plasmidfinder.tsv
- Download the card index necessary for the abricate2db.py script (aro_index.csv).
- run abricate2db.py - using all the previous tsv as input.
# e.g. abricate2db.py -i abr_plasmidfinder.tsv -db plasmidfinder \ -id 80 -cov 90 -csv aro_index.csv -db_psql <database_name>
Automation of this steps
This steps are fully automated in the nextflow pipeline pATLAS-db-creation.
Creating a custom version of pATLAS
If you require to add your own plasmids to pATLAS database
without asking to add them to pATLAS website,
you can provide custom fasta files when building the database using
-i option of MASHix.py.
Then follow the steps described above.
Run pATLAS locally
You can run pATLAS locally without much requirements by using patlas-compose. This will automatically handle the installation of the version 1.5.2 of pATLAS and launch the service in a local instance. For that you just require:
Then, follow this simple steps:
- Clone the repository patlas-compose.
git clone https://github.com/bfrgoncalves/patlas-compose
- Enter the patlas-compose folder
- Launch the compose:
Wait for the line
* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)to show up, meaning that the service is now running.
Note: This methodology is highly recommended.
pATLAS can be run locally if you have PostgreSQL installed and configured. After, you just need to:
- Clone this repository:
git clone https://github.com/tiagofilipe12/pATLAS
Create your custom database version or generate the default pATLAS database or download sql file from version 1.5.2 (the
tar.gzarchive). Note: if you download the sql file from version 1.5.2 you may skip steps 3 to 4 and continue with step 5.
Make sure all the necessary files are in place.
- by default pATLAS generates a
import_to_vivagraph.jsonfile in the folder
<tag_provided_to_o_flag>/results. Place this file in the
- change session to read the new
import_to_vivagraph.jsonfile by changing from
truea variable named
- Create the database that the front end will run:
Install backend dependencies:
# within the root directory of this repository pip install -r requirements.txt
- Install frontend dependencies:
# change directory to static direcoty where `index.html` will look for # its depdendenies cd patlas/db_manager/db_app/static/ # then install them (package.json is located in this directory) yarn install
- Compile node modules so that the html can understand, using webpack:
# You can also user a local installation of webpack. # entry-point.js is the config file where all the imported modules are # called node_modules/webpack/bin/webpack.js entry-point.js
- Then execute the script
# within the root directory of this repository cd patlas/db_manager ./run.py <your_database>
Note: the database name is utterly important to properly say to the frontend where to get the data.
- Go to
Optimization of the resources usage by the web page
devel = true isn't very efficient, so you can allow the
force directed graph to render in a
devel = true session, then when
you are satisfied pause the force layout using the buttons available in
pATLAS and click at the same time
Shift+Ctrl+Space. This will take a
while but eventually it will generate a file named
Once you have this file you can add it to the
patlas/db_manager/db_app/static/json folder and change the
devel variable to
false. This will use the previously saved
positions to render a pre rendered network.